Diagnostic techniques are generally employed to repair problems in complex devices such as, for example, an MFD (Multi-Function Device). Such diagnostic techniques can also be employed to identify and distinguish the cause of failure in a machine component from a failure symptom, as well as to predict the occurrence of a particular failure type from precursors.
An MFD is a rendering device or office machine, which incorporates the functionality of multiple devices in one apparatus or system, so as to have a smaller footprint in a home or small business setting, or to provide centralized document management/distribution/production in the context of, for example, a large-office setting. A typical MFP can provide a combination of some or all of the following capabilities: printer, scanner, photocopier, fax machine, e-mail capability, and so forth.
Problems that can be encountered with a fleet of MFDs before an MFD product is launched are often easily observable, repeatable, and diagnosable by an engineering team. Such problems can be repaired utilizing built-in diagnosis tools such as, for example, fault codes, electronic documents, and knowledge base documentation provided in association with the MFDs. Problems with indirect causes are more difficult to diagnose and repair.
Problems that occur with MFD products can sometimes be repaired based on a conversation with the customer as the first level of support. Conversations between customers and a servicing enterprise are often time consuming and costly for both the customer and the enterprise. Additionally, “help-desk” calls can be annoying for customers because much time is spent gathering preliminary information regarding the product before the essence of the problem is addressed.
Conventional diagnosis systems for identifying difficult problems involve the provision of device data (e.g., fault codes, status codes, usage counters and sensor readings) to a remote server in the form of an error report to figure out the problems remotely. The server couples the device data and the customer call, analyzes the transmitted data and provides a single response back to the user. Such prior art techniques for the classification of the device data with respect to a service action have succeeded with only a small fraction of the calls as the device data is noisy and uncertain.
Based on the foregoing, it is believed that a need exists for an improved remote diagnostic system and method. A need also exists for an improved classification technique for classifying a set of device data in accordance with a problem type, as described in greater detailed herein.